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  3. 通过深度特征学习在严格的隐私敏感环境中基于雷达的活动识别.
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  3. 通过深度特征学习在严格的隐私敏感环境中基于雷达的活动识别.

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通过深度特征学习在严格的隐私敏感环境中基于雷达的活动识别.

Giovanni Diraco1, Gabriele Rescio1, Alessandro Leone1

  • 1National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy.

Biomimetics (Basel, Switzerland)
|April 25, 2025

在PubMed 上查看摘要

概括
此摘要是机器生成的。

雷达技术为在卫生间等敏感区域识别人类活动提供了一个保护隐私的解决方案. 这个系统准确地识别日常生活活动,而不影响用户的隐私,与基于摄像头的方法不同.

关键词:
在FMCW雷达.深度特征学习 (Deep Feature Learning) 是一种深度特征学习.人类活动的认可 人类活动的认可隐私 隐私 隐私 隐私 隐私 隐私

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科学领域:

  • 工程 工程师 工程师 工程师
  • 计算机科学 计算机科学
  • 人与计算机的交互

背景情况:

  • 隐私问题限制了传统的基于视觉和可穿戴传感器的方法,用于在敏感环境中识别人类活动.
  • 非侵入性和匿名监控对于浴室等环境中的应用至关重要.

研究的目的:

  • 调查使用多普勒雷达用于识别隐私敏感的浴室环境中的人类活动的可行性.
  • 开发和评估使用雷达数据对日常生活活动进行分类的深度学习模型.

主要方法:

  • 使用BGT60TR13C Xensiv 60 GHz雷达传感器进行数据收集.
  • 从七名志愿者在浴室环境中收集了十项日常生活活动的数据集.
  • 采用深度学习模型,包括DenseNet201和ResNet50,使用双向长期短期记忆网络进行活动分类.

主要成果:

  • 实现了高的整体准确性,DenseNet201达到97.02%和ResNet50达到94.57%.
  • 在大多数活动中表现出强大的识别性能,包括洗脸,刷牙和穿衣/脱衣.
  • 鉴定"躺下"和"起床"作为具有挑战性的活动,由于运动相似性.

结论:

  • 基于多普勒雷达的人类活动识别是侵入性监控系统的可行和保护隐私的替代方案.
  • 拟议的雷达方法为需要非侵入性监控的智能家居和医疗保健应用提供了有效的解决方案.